
import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import DetaImageProcessor




class 					lowerCamelCase__				(							unittest.TestCase):




	'''simple docstring'''




	def __init__(   self						:Tuple	,  a						:Tuple	,  a						:List[str]=7	,  a						:Optional[int]=3	,  a						:Union[str, Any]=3_0	,  a						:Any=4_0_0	,  a						:Any=True	,  a						:Tuple=None	,  a						:Dict=True	,  a						:Any=[0.5, 0.5, 0.5]	,  a						:str=[0.5, 0.5, 0.5]	,  a						:Union[str, Any]=True	,  a						:Dict=1 / 2_5_5	,  a						:Optional[int]=True	,  )						->					Dict:
								# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
								__UpperCamelCase      :  Dict       =   size if size is not None else {"shortest_edge": 1_8, "longest_edge": 1_3_3_3}
								__UpperCamelCase      :  int       =   parent
								__UpperCamelCase      :  str       =   batch_size
								__UpperCamelCase      :  Union[str, Any]       =   num_channels
								__UpperCamelCase      :  Any       =   min_resolution
								__UpperCamelCase      :  Optional[int]       =   max_resolution
								__UpperCamelCase      :  Optional[Any]       =   do_resize
								__UpperCamelCase      :  Tuple       =   size
								__UpperCamelCase      :  Tuple       =   do_normalize
								__UpperCamelCase      :  List[str]       =   image_mean
								__UpperCamelCase      :  List[str]       =   image_std
								__UpperCamelCase      :  str       =   do_rescale
								__UpperCamelCase      :  Optional[Any]       =   rescale_factor
								__UpperCamelCase      :  List[str]       =   do_pad







	def   _lowerCamelCase							(   self						:List[str]		)						->					int:
								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_rescale": self.do_rescale,
								    "rescale_factor": self.rescale_factor,
								    "do_pad": self.do_pad,
								}







	def   _lowerCamelCase							(   self						:int	,  a						:Tuple	,  a						:Optional[Any]=False		)						->					str:
								if not batched:
															__UpperCamelCase      :  List[Any]       =   image_inputs[0]
															if isinstance(a	,  Image.Image		):
																						__UpperCamelCase  ,		__UpperCamelCase      :  List[str]       =   image.size
															else:
																						__UpperCamelCase  ,		__UpperCamelCase      :  Tuple       =   image.shape[1], image.shape[2]
															if w < h:
																						__UpperCamelCase      :  List[Any]       =   int(self.size["shortest_edge"] * h / w		)
																						__UpperCamelCase      :  Optional[Any]       =   self.size["shortest_edge"]
															elif w > h:
																						__UpperCamelCase      :  str       =   self.size["shortest_edge"]
																						__UpperCamelCase      :  Optional[Any]       =   int(self.size["shortest_edge"] * w / h		)
															else:
																						__UpperCamelCase      :  Optional[int]       =   self.size["shortest_edge"]
																						__UpperCamelCase      :  Optional[Any]       =   self.size["shortest_edge"]

								else:
															__UpperCamelCase      :  Optional[Any]       =   []
															for image in image_inputs:
																						__UpperCamelCase  ,		__UpperCamelCase      :  Any       =   self.get_expected_values([image]		)
																						expected_values.append((expected_height, expected_width)		)
															__UpperCamelCase      :  Optional[int]       =   max(a	,  key=lambda a		: item[0]		)[0]
															__UpperCamelCase      :  str       =   max(a	,  key=lambda a		: item[1]		)[1]

								return expected_height, expected_width




@require_torch
@require_vision
class 					lowerCamelCase__				(							__lowercase	,		unittest.TestCase):




	'''simple docstring'''




	_A								=	DetaImageProcessor if is_vision_available() else None







	def   _lowerCamelCase							(   self						:Any		)						->					List[Any]:
								__UpperCamelCase      :  str       =   DetaImageProcessingTester(self		)







	@property
	def   _lowerCamelCase							(   self						:List[str]		)						->					int:
								return self.image_processor_tester.prepare_image_processor_dict()







	def   _lowerCamelCase							(   self						:int		)						->					Any:
								__UpperCamelCase      :  List[str]       =   self.image_processing_class(**self.image_processor_dict		)
								self.assertTrue(hasattr(a	,  "image_mean"		)		)
								self.assertTrue(hasattr(a	,  "image_std"		)		)
								self.assertTrue(hasattr(a	,  "do_normalize"		)		)
								self.assertTrue(hasattr(a	,  "do_resize"		)		)
								self.assertTrue(hasattr(a	,  "do_rescale"		)		)
								self.assertTrue(hasattr(a	,  "do_pad"		)		)
								self.assertTrue(hasattr(a	,  "size"		)		)







	def   _lowerCamelCase							(   self						:Dict		)						->					Tuple:
								__UpperCamelCase      :  str       =   self.image_processing_class.from_dict(self.image_processor_dict		)
								self.assertEqual(image_processor.size	,  {"shortest_edge": 1_8, "longest_edge": 1_3_3_3}		)
								self.assertEqual(image_processor.do_pad	,  a		)







	def   _lowerCamelCase							(   self						:Union[str, Any]		)						->					Optional[int]:
								pass







	def   _lowerCamelCase							(   self						:Any		)						->					List[Any]:
								# Initialize image_processing
								__UpperCamelCase      :  List[Any]       =   self.image_processing_class(**self.image_processor_dict		)
								# create random PIL images
								__UpperCamelCase      :  Optional[int]       =   prepare_image_inputs(self.image_processor_tester	,  equal_resolution=a		)
								for image in image_inputs:
															self.assertIsInstance(a	,  Image.Image		)

								# Test not batched input
								__UpperCamelCase      :  List[str]       =   image_processing(image_inputs[0]	,  return_tensors="pt"		).pixel_values

								__UpperCamelCase  ,		__UpperCamelCase      :  List[Any]       =   self.image_processor_tester.get_expected_values(a		)

								self.assertEqual(
								    encoded_images.shape	,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,  )

								# Test batched
								__UpperCamelCase  ,		__UpperCamelCase      :  Optional[int]       =   self.image_processor_tester.get_expected_values(a	,  batched=a		)

								__UpperCamelCase      :  List[Any]       =   image_processing(a	,  return_tensors="pt"		).pixel_values
								self.assertEqual(
								    encoded_images.shape	,  (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        expected_height,
								        expected_width,
								    )	,  )







	def   _lowerCamelCase							(   self						:Optional[Any]		)						->					Tuple:
								# Initialize image_processing
								__UpperCamelCase      :  Dict       =   self.image_processing_class(**self.image_processor_dict		)
								# create random numpy tensors
								__UpperCamelCase      :  List[Any]       =   prepare_image_inputs(self.image_processor_tester	,  equal_resolution=a	,  numpify=a		)
								for image in image_inputs:
															self.assertIsInstance(a	,  np.ndarray		)

								# Test not batched input
								__UpperCamelCase      :  Dict       =   image_processing(image_inputs[0]	,  return_tensors="pt"		).pixel_values

								__UpperCamelCase  ,		__UpperCamelCase      :  Dict       =   self.image_processor_tester.get_expected_values(a		)

								self.assertEqual(
								    encoded_images.shape	,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,  )

								# Test batched
								__UpperCamelCase      :  Optional[int]       =   image_processing(a	,  return_tensors="pt"		).pixel_values

								__UpperCamelCase  ,		__UpperCamelCase      :  List[str]       =   self.image_processor_tester.get_expected_values(a	,  batched=a		)

								self.assertEqual(
								    encoded_images.shape	,  (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        expected_height,
								        expected_width,
								    )	,  )







	def   _lowerCamelCase							(   self						:List[Any]		)						->					str:
								# Initialize image_processing
								__UpperCamelCase      :  List[Any]       =   self.image_processing_class(**self.image_processor_dict		)
								# create random PyTorch tensors
								__UpperCamelCase      :  int       =   prepare_image_inputs(self.image_processor_tester	,  equal_resolution=a	,  torchify=a		)
								for image in image_inputs:
															self.assertIsInstance(a	,  torch.Tensor		)

								# Test not batched input
								__UpperCamelCase      :  int       =   image_processing(image_inputs[0]	,  return_tensors="pt"		).pixel_values

								__UpperCamelCase  ,		__UpperCamelCase      :  int       =   self.image_processor_tester.get_expected_values(a		)

								self.assertEqual(
								    encoded_images.shape	,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,  )

								# Test batched
								__UpperCamelCase      :  str       =   image_processing(a	,  return_tensors="pt"		).pixel_values

								__UpperCamelCase  ,		__UpperCamelCase      :  Tuple       =   self.image_processor_tester.get_expected_values(a	,  batched=a		)

								self.assertEqual(
								    encoded_images.shape	,  (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        expected_height,
								        expected_width,
								    )	,  )







	@slow
	def   _lowerCamelCase							(   self						:Dict		)						->					str:
								# prepare image and target
								__UpperCamelCase      :  str       =   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
								with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"	,  "r"		) as f:
															__UpperCamelCase      :  int       =   json.loads(f.read()		)

								__UpperCamelCase      :  Any       =   {"image_id": 3_9_7_6_9, "annotations": target}

								# encode them
								__UpperCamelCase      :  int       =   DetaImageProcessor()
								__UpperCamelCase      :  str       =   image_processing(images=a	,  annotations=a	,  return_tensors="pt"		)

								# verify pixel values
								__UpperCamelCase      :  List[str]       =   torch.Size([1, 3, 8_0_0, 1_0_6_6]		)
								self.assertEqual(encoding["pixel_values"].shape	,  a		)

								__UpperCamelCase      :  Optional[int]       =   torch.tensor([0.2796, 0.3138, 0.3481]		)
								self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]	,  a	,  atol=1E-4		)		)

								# verify area
								__UpperCamelCase      :  List[str]       =   torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]		)
								self.assertTrue(torch.allclose(encoding["labels"][0]["area"]	,  a		)		)
								# verify boxes
								__UpperCamelCase      :  Any       =   torch.Size([6, 4]		)
								self.assertEqual(encoding["labels"][0]["boxes"].shape	,  a		)
								__UpperCamelCase      :  Optional[int]       =   torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]		)
								self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]	,  a	,  atol=1E-3		)		)
								# verify image_id
								__UpperCamelCase      :  Any       =   torch.tensor([3_9_7_6_9]		)
								self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]	,  a		)		)
								# verify is_crowd
								__UpperCamelCase      :  Tuple       =   torch.tensor([0, 0, 0, 0, 0, 0]		)
								self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]	,  a		)		)
								# verify class_labels
								__UpperCamelCase      :  int       =   torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]		)
								self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]	,  a		)		)
								# verify orig_size
								__UpperCamelCase      :  Dict       =   torch.tensor([4_8_0, 6_4_0]		)
								self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]	,  a		)		)
								# verify size
								__UpperCamelCase      :  Any       =   torch.tensor([8_0_0, 1_0_6_6]		)
								self.assertTrue(torch.allclose(encoding["labels"][0]["size"]	,  a		)		)







	@slow
	def   _lowerCamelCase							(   self						:Optional[Any]		)						->					Dict:
								# prepare image, target and masks_path
								__UpperCamelCase      :  Any       =   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
								with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"	,  "r"		) as f:
															__UpperCamelCase      :  Tuple       =   json.loads(f.read()		)

								__UpperCamelCase      :  Optional[Any]       =   {"file_name": "000000039769.png", "image_id": 3_9_7_6_9, "segments_info": target}

								__UpperCamelCase      :  Optional[Any]       =   pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"		)

								# encode them
								__UpperCamelCase      :  str       =   DetaImageProcessor(format="coco_panoptic"		)
								__UpperCamelCase      :  Tuple       =   image_processing(images=a	,  annotations=a	,  masks_path=a	,  return_tensors="pt"		)

								# verify pixel values
								__UpperCamelCase      :  Tuple       =   torch.Size([1, 3, 8_0_0, 1_0_6_6]		)
								self.assertEqual(encoding["pixel_values"].shape	,  a		)

								__UpperCamelCase      :  str       =   torch.tensor([0.2796, 0.3138, 0.3481]		)
								self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]	,  a	,  atol=1E-4		)		)

								# verify area
								__UpperCamelCase      :  str       =   torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]		)
								self.assertTrue(torch.allclose(encoding["labels"][0]["area"]	,  a		)		)
								# verify boxes
								__UpperCamelCase      :  List[Any]       =   torch.Size([6, 4]		)
								self.assertEqual(encoding["labels"][0]["boxes"].shape	,  a		)
								__UpperCamelCase      :  str       =   torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]		)
								self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]	,  a	,  atol=1E-3		)		)
								# verify image_id
								__UpperCamelCase      :  Optional[Any]       =   torch.tensor([3_9_7_6_9]		)
								self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]	,  a		)		)
								# verify is_crowd
								__UpperCamelCase      :  int       =   torch.tensor([0, 0, 0, 0, 0, 0]		)
								self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]	,  a		)		)
								# verify class_labels
								__UpperCamelCase      :  str       =   torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]		)
								self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]	,  a		)		)
								# verify masks
								__UpperCamelCase      :  Tuple       =   8_2_2_8_7_3
								self.assertEqual(encoding["labels"][0]["masks"].sum().item()	,  a		)
								# verify orig_size
								__UpperCamelCase      :  int       =   torch.tensor([4_8_0, 6_4_0]		)
								self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]	,  a		)		)
								# verify size
								__UpperCamelCase      :  Optional[int]       =   torch.tensor([8_0_0, 1_0_6_6]		)
								self.assertTrue(torch.allclose(encoding["labels"][0]["size"]	,  a		)		)

import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
		import tensorflow as tf

if is_tensorflow_text_available():
		from transformers.models.bert import TFBertTokenizer


lowercase    :   Union[str, Any]						=			['bert-base-uncased', 'bert-base-cased']
lowercase    :   Any						=			'hf-internal-testing/tiny-bert-tf-only'

if is_tf_available():




		class 					lowerCamelCase__				(							tf.keras.Model):




			'''simple docstring'''




			def __init__(   self						:List[Any]	,  a						:int		)						->					int:
										super().__init__()
										__UpperCamelCase      :  Any       =   tokenizer
										__UpperCamelCase      :  Union[str, Any]       =   AutoConfig.from_pretrained(a		)
										__UpperCamelCase      :  Dict       =   TFAutoModel.from_config(a		)







			def   _lowerCamelCase							(   self						:List[Any]	,  a						:Dict		)						->					Any:
										__UpperCamelCase      :  int       =   self.tokenizer(a		)
										__UpperCamelCase      :  int       =   self.bert(**a		)
										return out["pooler_output"]




@require_tf
@require_tensorflow_text
class 					lowerCamelCase__				(							unittest.TestCase):




	'''simple docstring'''




	def   _lowerCamelCase							(   self						:str		)						->					str:
								super().setUp()

								__UpperCamelCase      :  Dict       =   [
								    BertTokenizer.from_pretrained(a		) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
								]  # repeat for when fast_bert_tokenizer=false
								__UpperCamelCase      :  List[Any]       =   [TFBertTokenizer.from_pretrained(a		) for checkpoint in TOKENIZER_CHECKPOINTS] + [
								    TFBertTokenizer.from_pretrained(a	,  use_fast_bert_tokenizer=a		)
								    for checkpoint in TOKENIZER_CHECKPOINTS
								]
								assert len(self.tokenizers		) == len(self.tf_tokenizers		)

								__UpperCamelCase      :  Dict       =   [
								    "This is a straightforward English test sentence.",
								    "This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.",
								    "Now we're going to add some Chinese: 一 二 三 一二三",
								    "And some much more rare Chinese: 齉 堃 齉堃",
								    "Je vais aussi écrire en français pour tester les accents",
								    "Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ",
								]
								__UpperCamelCase      :  Dict       =   list(zip(self.test_sentences	,  self.test_sentences[::-1]		)		)







	def   _lowerCamelCase							(   self						:List[Any]		)						->					int:
								for tokenizer, tf_tokenizer in zip(self.tokenizers	,  self.tf_tokenizers		):
															for test_inputs in (self.test_sentences, self.paired_sentences):
																						__UpperCamelCase      :  str       =   tokenizer(a	,  return_tensors="tf"	,  padding="longest"		)
																						__UpperCamelCase      :  Any       =   tf_tokenizer(a		)

																						for key in python_outputs.keys():
																													self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape		)		)
																													self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key]	,  tf.intaa		) == tf_outputs[key]		)		)







	@slow
	def   _lowerCamelCase							(   self						:Union[str, Any]		)						->					Any:
								for tf_tokenizer in self.tf_tokenizers:
															__UpperCamelCase      :  List[str]       =   tf_tokenizer(self.paired_sentences		)
															__UpperCamelCase      :  Union[str, Any]       =   tf_tokenizer(
															    text=[sentence[0] for sentence in self.paired_sentences]	,  text_pair=[sentence[1] for sentence in self.paired_sentences]	,  )
															for key in merged_outputs.keys():
																						self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key]	,  tf.intaa		) == separated_outputs[key]		)		)







	@slow
	def   _lowerCamelCase							(   self						:Any		)						->					Optional[int]:
								for tf_tokenizer in self.tf_tokenizers:
															__UpperCamelCase      :  Optional[Any]       =   tf.function(a		)
															for test_inputs in (self.test_sentences, self.paired_sentences):
																						__UpperCamelCase      :  Tuple       =   tf.constant(a		)
																						__UpperCamelCase      :  List[str]       =   compiled_tokenizer(a		)
																						__UpperCamelCase      :  Any       =   tf_tokenizer(a		)

																						for key in eager_outputs.keys():
																													self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]		)		)







	@slow
	def   _lowerCamelCase							(   self						:List[Any]		)						->					Tuple:
								for tf_tokenizer in self.tf_tokenizers:
															__UpperCamelCase      :  str       =   ModelToSave(tokenizer=a		)
															__UpperCamelCase      :  List[Any]       =   tf.convert_to_tensor(self.test_sentences		)
															__UpperCamelCase      :  Optional[int]       =   model(a		)  # Build model with some sample inputs
															with TemporaryDirectory() as tempdir:
																						__UpperCamelCase      :  Optional[Any]       =   Path(a		) / "saved.model"
																						model.save(a		)
																						__UpperCamelCase      :  List[Any]       =   tf.keras.models.load_model(a		)
															__UpperCamelCase      :  Optional[Any]       =   loaded_model(a		)
															# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
															self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output		)		)	,  1E-5		)